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Found 910 results
Author [ Title(Desc)] Type Year
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Janner, M., Wu, J., Kulkarni, T., Yildirim, I. & Tenenbaum, J. B. Self-supervised intrinsic image decomposition. Annual Conference on Neural Information Processing Systems (NIPS) (2017). at <https://papers.nips.cc/paper/7175-self-supervised-intrinsic-image-decomposition>PDF icon intrinsicImg_nips_2017.pdf (5.87 MB)
Wang, J. & Yuille, A. Semantic Part Segmentation using Compositional Model combing Shape and Appearance. CVPR (2015).PDF icon JianyuWangSemanticCVPR2015 (1).pdf (6.15 MB)
Singer, J., Madsen, J., Anderson, W. S. & Kreiman, G. Sensitivity to timing and order in human visual cortex. Journal of Neurophysiology 113, 1656 - 1669 (2015).
Singer, J., Madsen, J., Anderson, W. S. & Kreiman, G. Sensitivity to Timing and Order in Human Visual Cortex. (2014).PDF icon CBMM-Memo-005.pdf (1.12 MB)
Galanti, T., Siegel, Z., Gupte, A. & Poggio, T. SGD and Weight Decay Provably Induce a Low-Rank Bias in Deep Neural Networks. (2023).PDF icon Low-rank bias.pdf (2.38 MB)
Galanti, T. & Poggio, T. SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks. (2022).PDF icon Implicit Rank Minimization.pdf (1.76 MB)
zhang, zhoutong et al. Shape and Material from Sound. Advances in Neural Information Processing Systems 30 1278–1288 (2017). at <http://papers.nips.cc/paper/6727-shape-and-material-from-sound.pdf>
Adhya, D. et al. Shared gene co-expression networks in autism from induced pluripotent stem cell (iPSC) neurons. BioRxiv (2018). doi:10.1101/349415
Mendoza-Halliday, D., Torres, S. & Martinez-Trujillo, J. Sharp emergence of feature-selective sustained activity along the dorsal visual pathway. Nature Neuroscience 7, (2014).
Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. (2014). at <http://klab.tch.harvard.edu/resources/singer_asynchrony.html>
Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. J Vis 14, 7 (2014).
Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. Journal of Vision 12, (2014).
Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. (2014). at <http://klab.tch.harvard.edu/resources/singer_asynchrony.html>
Dapello, J. et al. Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020). at <https://proceedings.neurips.cc/paper/2020/hash/98b17f068d5d9b7668e19fb8ae470841-Abstract.html>
Prevedel, R. et al. Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy. Nature Methods 11, 727 - 730 (2014).
Fried, I., Rutishauser, U., Cerf, M. & Kreiman, G. Single Neuron Studies of the Human Brain. Probing Cognition. Probing cognition (MIT Press, 2014).
Arend, L. et al. Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).PDF icon CBMM-Memo-093.pdf (2.99 MB)
Zhang, Z. et al. Single-Shot Object Detection with Enriched Semantics. Conference on Computer Vision and Pattern Recognition (CVPR) (2018). at <http://cvpr2018.thecvf.com/>
Zhang, Z. et al. Single-Shot Object Detection with Enriched Semantics. (2018).PDF icon CBMM-Memo-084.pdf (1.92 MB)
Liu, S. & Spelke, E. S. Six-month-old infants expect agents to minimize the cost of their actions. Cognition 160, 35-42 (2017).
Liu, S. & Spelke, E. S. Six-month-old infants represent action efficiency on a continuous scale. 9th Biennial Meeting of the Cognitive Development Society (CDS) (2015).
Golowich, N., Rakhlin, A. & Shamir, O. Size-Independent Sample Complexity of Neural Networks. (2017).PDF icon 1712.06541.pdf (278.77 KB)
Xie, Y., Li, Y. & Rangamani, A. Skip Connections Increase the Capacity of Associative Memories in Variable Binding Mechanisms. (2023).PDF icon CBMM-Memo-142.pdf (1.64 MB)
Freiwald, W. A. Social interaction networks in the primate brain. Current Opinion in Neurobiology 65, 49 - 58 (2020).
Tejwani, R., Kuo, Y. - L., Shu, T., Katz, B. & Barbu, A. Social Interactions as Recursive MDPs. (2021).PDF icon CBMM-Memo-130.pdf (1.52 MB)

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